Development of statistical models for knowledge acquisition from large-scale data including multiform samples

作为理论观点，我们提出了以下模型，并使用统计语言s开发了这些方法的原型程序。(1)我们提出了一个递归划分线性模型，该模型可以转换为有序线性模型，基于线性项的树回归模型（Seki & Tsutsui 98）。(2)假设传感器技术的发展可以持续监测零件的劣化特征，提出了一种过程监控图来监控零件是否发生不规则劣化。(3)提出了一种估计最近邻型非参数回归模型的方法，通过优化加权欧几里得距离的权重，以考虑解释变量水平的影响。(4)在给定两个解释变量集的情况下，提出了一种通过区分响应变量与解释变量之间关系相似的类别对样本集进行分层的方法，一个解释变量集可用于样本集的分层，另一个解释变量集可用于响应变量的回归。(5)我们提出了一个使用最小描述长度标准来比较剂量水平的非参数检验。为了验证提出的POS （Point Of Sales）数据与客户ID的方法，我们参加了由日本运筹学协会等主办的数据分析比赛，并获得了以下结果。2001年某食品超市数据，大赛冠军；2002年某百货公司数据，分会斗志奖；2003年3家百货公司数据，分会优秀奖

As theoretical viewpoints, we proposed the following models and developed the prototype programs of these methods with the statistical language S.(1)We proposed a recursive partitioning linear model, which can be transformed to ordinal linear models, based on tree regression model with linear terms (Seki & Tsutsui 98).(2)We proposed a process monitoring chart to monitor the parts whether irregular deterioration occurred, supposing that the deterioration characteristic can be continuously monitored by development of sensor technology.(3)We proposed a method to estimate a Nearest Neighbor type non-parametric regression model, by optimizing the weight of the weighted Euclidian distance, in order to take into consideration the levels of explanation variables effect.(4)We proposed a method which stratifies sample set by discriminating the class in which the relation between the response variable and explanation variables are similar, in the case where two explanation variable sets are given : one can be used for stratification of sample set, and another can be used for regression of response variable.(5)We proposed a non-parametric, test using Minimum Description length criterion for comparing dose levels. In order to verify proposed methodology about POS (Point Of Sales) data with customer ID, we participate in the data analysis competition sponsored by the Operations Research Society of Japan etc., and obtained the following results.2001 a food supermarket's data, competition championship2002 a department store's data, Sectional-meeting fighting spirit award2003 3 department stores' data, Sectional-meeting superior prize